Evidence map›Paper›PMID 41758510›Full record

ArticleJAMA network open2026

Multicenter Prospective Validation of an Updated Proprietary Sepsis Prediction Model.

Andrew Wong, Danielle Currey, Megan Schwinne, Brenna Park-Egan, Sean Meyer, Andrew Gutting, Jie Cao, Sharaf Khan, Raymund Dantes, Tony Pan and 6 more

Abstract readMulticenter StudyValidation Study
In one paragraph

Article in JAMA network open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Andrew WongUniversity of Michigan Medical School, Ann Arbor.
Danielle CurreyCase Western Reserve University School of Medicine, Cleveland, Ohio.
Megan SchwinneDepartment of Biomedical Informatics, Emory University School of Medicine, Atlanta, Georgia.
Brenna Park-EganDivision of Pulmonary, Allergy, and Critical Care Medicine, Oregon Health & Science University, Portland.
Sean MeyerUniversity of Michigan Medical School, Ann Arbor.
Andrew GuttingUniversity of Michigan Medical School, Ann Arbor.
Jie CaoJoan and Irwin Jacobs Center for Health Innovation, UC San Diego Health, San Diego, California.
Sharaf KhanDepartment of Emergency Medicine, Emory University School of Medicine, Atlanta, Georgia.
Raymund DantesDepartment of Medicine, Emory University School of Medicine, Atlanta, Georgia.
Tony PanDepartment of Biomedical Informatics, Emory University School of Medicine, Atlanta, Georgia.
Timothy BuchmanDepartment of Surgery, Emory University School of Medicine, Atlanta, Georgia.
Karandeep SinghJoan and Irwin Jacobs Center for Health Innovation, UC San Diego Health, San Diego, California.
Sivasubramanium V BhavaniDepartment of Medicine, Emory University School of Medicine, Atlanta, Georgia.
Patrick G LyonsDivision of Pulmonary, Allergy, and Critical Care Medicine, Oregon Health & Science University, Portland.
Michael W SjodingUniversity of Michigan Medical School, Ann Arbor.
Yasir TarabichiCase Western Reserve University School of Medicine, Cleveland, Ohio.

Funding

Optimizing Clinician and AI Collaborations to Support Timely Management of Acute DyspneaR01HL158626 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Michael William Sjoding, Jenna Wiens · 2021 to 2026
$3.8M
SPOT-IT: Sepsis Prediction in Oncology Through Implementation Science and TechnologyK08CA270383 · NCI · OREGON HEALTH & SCIENCE UNIVERSITY · PI Patrick G Lyons · 2024 to 2026
$851k
NCI NIH HHS K08 CA270383NHLBI NIH HHS R01 HL158626
6 · The paper itself

Abstract

Importance: The Epic Sepsis Model version 2 (ESM v2) is a widely implemented proprietary sepsis prediction model, but no multicenter, external validation of its performance has been reported to guide adoption and use. Objective: To conduct a multicenter validation of the ESM v2 to compare performance against the original ESM v1, outline differences across heterogenous clinical sites, and compare model performance against clinician recognition of sepsis. Design, Setting, and Participants: This prognostic study included adult inpatient encounters at 4 large US health systems between August 31, 2023, and March 11, 2025. At each site, data were collected for a consecutive period immediately following new model implementation. Data were analyzed from July 23 to August 19, 2025. Main Outcomes and Measures: Sepsis was defined using Sepsis-3 clinical consensus criteria. Model discrimination was assessed using area under the receiver operating characteristic curve (AUROC) at the encounter level and prediction level with 4-hour, 12-hour, and hospitalization-wide time horizons. Performance against clinician recognition of sepsis was measured using antibiotics, lactate, and body culture orders. Results: Of 227 091 inpatient encounters, 7401 (3.3%; median [IQR] age, 65 [54-75] years; 3359 [45.4%] female; 2.7% Asian; 24.6% Black; 64.6% White; 7.1% Hispanic ethnicity) met sepsis criteria and 219 690 (96.7%; median [IQR] age, 48 [33-65] years; 123 563 [56.2%] female; 2.5% Asian; 38.8% Black; 49.6% White; 9.6% Hispanic ethnicity) did not. At the encounter level, the AUROC ranged from 0.82 (95% CI, 0.81-0.83) to 0.92 (95% CI, 0.92-0.93) across study sites. At the prediction level with a 12-hour time horizon, the AUROC ranged from 0.75 (95% CI, 0.74-0.75) to 0.85 (95% CI, 0.85-0.85). Comparison against clinician recognition of sepsis yielded a minor decrease in performance, with the resulting encounter-level AUROC ranging from 0.80 (95% CI, 0.79-0.81) to 0.90 (95% CI, 0.89-0.90) across sites. Positive predictive values remained low, from 0.13 (95% CI, 0.13-0.14) to 0.26 (95% CI, 0.25-0.27), with a high number needed to evaluate and high alert burden. Conclusions and Relevance: In this prognostic study of a new sepsis prediction model, a multicenter prospective validation performed across 4 major US health systems found improved discrimination for the early prediction of sepsis but noted high institutional variability, low positive predictive value, and high alert burden.

Indexed as

Prediction AlgorithmsSepsisAgedFemaleHumansMaleMiddle AgedPrognosisProspective StudiesROC CurveUnited States

Identifiers

PMID41758510
PMCPMC12949446

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.